Abstract

OBJECTIVES: To evaluate and characterize the Global Trigger Tool’s (GTT's) utility in a pediatric population; to measure the rate of harm at our institution and compare it with previously established trigger tools and benchmark rates; and to describe the distribution of harm of the detected events.

METHODS: Per the GTT methodology, 240 random inpatient charts were retrospectively reviewed over a 12-month pilot period for the presence of 53 predefined safety triggers. When triggers were detected, the reviewers investigated the chart more thoroughly to decide whether an adverse event occurred. Agreement with a physician reviewer was then reached, and a level of harm was assigned.

RESULTS: A total of 404 triggers were detected (1.7 triggers per patient), and 88 adverse events were identified. Rates of 36.7 adverse events per 100 admissions and 76.3 adverse events per 1000 patient-days were calculated. Sixty-two patients (25.8%) had at least 1 adverse event during their hospitalization, and 18 (7.5%) had >1 event identified. Three-quarters of the events were category E (temporary harm). Two events required intervention to sustain life (category H). Two of the 6 trigger modules identified 95% of the adverse events.

CONCLUSIONS: The GTT demonstrated utility in the pediatric inpatient setting. With the use of the trigger tool, we identified a rate of harm 2 to 3 times higher than previously published pediatric rates. Modifications to the trigger tool to address pediatric-specific issues could increase the test characteristics of the tool.

National Coordinating Council for Medication Error Reporting and Prevention

What’s Known on This Subject:

The Global Trigger Tool uses a sampling methodology to identify and measure harm rates. It has been shown to effectively detect adverse events when applied in the adult environment, but it has never been evaluated in a pediatric setting.

What This Study Adds:

The Global Trigger Tool can be used in the pediatric inpatient environment to measure adverse safety events. We detected a 2 to 3 times higher harm rate than previously found with different metrics in this setting.

Serving as a catalyst for increased public awareness and national dialogues on patient safety, the groundbreaking report To Err is Human: Building a Safer Health System1 continues to provide momentum for patient safety-focused improvement initiatives at national and local levels, with increased public awareness of patient care errors and demand for accountability.

In an effort to accept responsibility for improvement, a number of strategies to detect patient harm have emerged, ranging from voluntary reporting to structured and focused retrospective chart review.2–9 However, despite rigorous safety improvement efforts and interventions adopted since To Err’s release, a recent study indicated no evidence of reduced patient harm among a group of North Carolina hospitals during a 5-year period.10

Manual medical record review trigger tool methodologies have shown much promise in increasing detection of adverse events (AEs).11–13 Although most trigger tools are adult focused, pediatric examples include the Child Health Corporation of America tool, the National Health Services Pediatric Trigger Tool, and the recently validated and published Canadian Trigger Tool.4,6,14,15

To improve efforts to detect harm by the use of trigger tool methodology, the Institute for Healthcare Improvement (IHI) developed the Global Trigger Tool (GTT).11,13,16 The GTT has a specific methodology to detect patient harm based on a retrospective examination of medical records by trained reviewers. A trigger is an occurrence, prompt, or flag in the medical record that may represent an AE and serve as a clue or indication for reviewers to investigate the record more thoroughly. The 53 triggers in the GTT are grouped into 6 categories referred to as modules: Cares, Medication, Surgical, Emergency Department, Intensive Care, and Perinatal. Using the GTT, Classen et al found at least 10 times more confirmed, serious events than voluntary safety reports or the Agency for Healthcare Research and Quality’s Patient Safety Indicators. The GTT had a higher sensitivity to detect patients with at least 1 AE (94.9%) and a higher specificity (100%) to detect patients with no AEs.17,18

Although several studies have evaluated the GTT's utility in the adult inpatient population, it has never been evaluated in a pediatric setting.8,19,20 Takata et al15 developed a pediatric-focused trigger tool based upon an earlier version of the IHI GTT. However, the tool used 15 triggers and focused on adverse drug events (ADEs) only.

We used the complete, adult-focused GTT to identify adverse events in a pediatric tertiary care setting. Our aims were to (1) evaluate and characterize the GTT’s utility in a pediatric population, (2) measure the rate of AE harm at our institution and compare our rate with previously reported trigger tool and benchmark rates, and (3) describe the distribution of harm of the detected AEs.

Methods

Setting

Cincinnati Children’s Hospital Medical Center (CCHMC) is a large, urban pediatric academic medical center with 523 registered beds and >32 000 admissions in fiscal year 2010. CCHMC has a comprehensive electronic health record system, including electronic documentation. The study team consisted of a physician, 3 registered nurses, an outcomes manager, a project manager, and a data analyst.

Definitions

We used IHI’s definition of harm: “unintended physical injury resulting from or contributed by medical care that requires additional monitoring, treatment, or hospitalization, or that results in death.”16 IHI further defines events causing harm as including only those AEs related to the active delivery of harm (commission) and excludes issues related to substandard care (omission).

The definition of ADE, a subgroup of AEs, came from the World Health Organization: “Noxious and unintended and occurs at doses used in man for prophylaxis, diagnostic, therapy, or modification of physiologic function.”21

Planning the Intervention

Before beginning the 12-month pilot study, 2 nurse chart reviewers completed IHI training on the use of the GTT methodology consisting of 3 Internet-based conference calls during which IHI leaders described how to detect triggers and determine whether a trigger indicated an AE of patient harm.16 The nurse reviewers also had previous experience with similar medical record review methodology adopted by Child Health Corporation of America.15

The reviewers completed case studies for detecting triggers and AEs during 6 training sessions delivered at monthly intervals. Data results were discussed internally and compared with the IHI interpretations at the next session. Our reviewers discussed any discrepancies among themselves to reach agreement on conditions causing harm.

Intervention

Each month in 2009, the nurse reviewers and physician on the team used the GTT to review a random sample of 20 inpatient medical records (Fig 1). Inclusion criteria were as follows: discharged inpatients of any age whose records were closed, admission of at least 24 hours, and a discharge date >30 days before the scheduled review date. Any patients who were admitted for rehabilitation or psychiatric care were excluded. Each month, the data analyst identified all records that met selection criteria and used a random-number generator to assign each record a number. The 20 inpatient records selected each month were based on the smallest assigned random numbers. From the randomized list of records, the first 20 meeting inclusion and exclusion criteria were chosen for review. All records were available in electronic format.

Following the IHI protocol, the 2 trained reviewers read each record independently, limiting the review to 20 minutes regardless of length of stay or medical complexity.16 If none of the 53 GTT triggers were identified, the review process for that record was complete. However, if one or more triggers were noted, a more focused review of the record was conducted to determine if an AE had occurred based on the definition and criteria established by the IHI protocol. For each AE identified, a level of harm was assigned by using criteria established by the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP).22 Multiple triggers could be linked to a single AE, with triggers occurring in any of the GTT modules. Preventability was not determined for detected adverse events, as per the IHI GTT protocol.

After their independent review of the 20 records each month, the nurse reviewers met to compare their results and reach consensus on the identification of triggers, AEs, and harm. They then met with the physician member of the review team, who made the final decision of whether an AE occurred and the severity of harm associated with that event. Validated data for each month were entered into a Microsoft Access database for analysis.

IHI adult benchmark event rates were examined as a comparative measure to evaluate the fit of the tool in a pediatric environment. Similarly, patient characteristic data provided a validation of the selection process and a means for describing the pediatric environment being studied.

Analysis

Outcome measures were standardized to patient days and number of admissions to permit comparison over time and with previously published reports. Statistical process control run charts23 were used to examine the change in AEs over time and compare our results with IHI benchmark data. Student t tests were used to identify differences between our results and benchmark AE rates.

Process measures were examined to determine compliance with IHI standards for the trigger tool process and data validity. Interrater reliability, using the Cohen κ, was calculated by using a 3-month sample from July to September 2009 to measure the agreement between the nurse reviewers and with the physician validator and as an indicator of review consistency and process compliance. Student t tests for validity in length of stay, patient age, and gender and Pearson χ2 test for goodness of fit for race and ethnicity, evaluated at a 95% confidence level, were used to validate the chart selection process. Secondary analyses of patients with a single AE or multiple AEs were conducted to provide an understanding of the variation and likelihood that a patient would experience an AE.

Human Subject Protection

The Institutional Review Board at CCHMC approved this project as part of the hospital’s quality improvement activities. Written informed consent was not required provided that no individual patients or providers were identified.

Results

Patient Characteristics

The charts of 240 randomly selected patients, reflecting 1206 patient-days, were reviewed. The characteristics of these patients were similar to those of all patients at CCHMC during this time (Table 1). Of the 240 randomly selected patients, 78 patients (32.5%) went to the operating room during their hospital stay, and 32 patients (13.3%) spent a portion of their stay in an ICU.

A total of 404 triggers were detected. Examples of detected triggers are shown in Table 2. The mean rate of triggers per patient was 1.7 (Table 3). Eighty-eight AEs were identified, for a mean rate of 36.7 AEs per 100 patients and 76.3 AEs per 1000 patient-days. Sixty-two patients had at least 1 AE during their hospitalization, and 18 had >1 AE identified.

Our average rate of AEs per 100 admissions, at 36.7, was 8.3% below the IHI adult benchmark rate of 40 (Fig 2A), and our rate of AEs per 1000 patient-days, at 76.3, was 15.3% below the benchmark rate of 90 (Fig 2B).16 Both rates were statistically equivocal with the IHI benchmarks (P values of .48 and .14, respectively). Table 4 compares our AE and ADE rates with those previously published in pediatric populations.6,15,24,25

Ninety-five percent of AEs were identified from triggers in 2 GTT modules: Cares and Medication (Table 5). The Cares module triggers identified 53 (60%) AEs, with 7 categorized as “Other.” Other events were related to clinical care activities, but not identified through the more specific GTT triggers. A list of AEs detected from Other triggers is available in Supplemental Table 6. Of the 60 AEs identified by the Medication module triggers, 18 were linked to Other triggers, indicating a relationship with a medication-related AE that was not captured by the GTT triggers. One general trigger in the Surgical module (S11, Any operative complication) identified 4 AEs (5%). The Emergency Department, Intensive Care, and Perinatal modules identified 26 triggers, but no AEs were detected.

Interrater Reliability

The nurse reviewers had substantial agreement in their assessment of the presence or absence of an AE in the patient records (κ = 0.63). Their combined agreement with the physician validator on the presence or absence of the AE was near perfect (κ = 0.85).

Discussion

To our knowledge, this is the first published report of the application of the well-studied GTT to a pediatric population. This study also represents the largest published manual trigger tool record sampling from a single pediatric institution to date.

Our findings of 36.7 AEs and 25.0 ADEs per 100 patients and 76.3 AEs and 49.8 ADEs per 1000 patient-days were >2 to 3 times previously published rates by using trigger tools in the pediatric population. This was despite having a 30% lower trigger-per-patient rate by using the GTT than Takata et al reported with their ADE-only trigger tool (1.7 triggers per patient versus 2.5).15 The GTT detected more medication-related AEs than the Takata et al study found (Table 4: 25.0 ADEs versus 11.1 ADEs per 100 patients, 49.8 ADEs versus 15.7 ADEs per 1000 patient-days). Takata’s methodology was restricted to detecting ADEs and did not address nonmedication AEs, whereas the GTT was constructed to detect both ADEs and (nonmedication) AEs.

We believe the GTT yielded much higher AE rates because the methodology allows for inclusion of Other triggers. These triggers are placeholders for AEs that are not associated with other, more specific, triggers. Their inclusion in a trigger tool reminds chart reviewers to look for other types of AEs and record them for further investigation, tracking, and consideration for future safety initiatives. Twenty-nine (33%) of the discovered AEs fell under 3 Other triggers, likely reflecting the fact that many pediatric AEs are not represented by specific triggers in the GTT. Peripheral intravenous catheter-related AEs, such as infiltrates and phlebitis, are examples of triggers that are not included in the current GTT but occur fairly frequently at CCHMC. Adding a peripheral intravenous infiltrate/phlebitis trigger to future pediatric trigger tools would remind chart reviewers that these incidents are indeed AEs and, as such, should be included in calculations of rates of harm.

Most pediatric trigger tools used at present do not include AEs that are found incidentally. One exception is the newly validated Canadian Pediatric Trigger Tool. It has 1 generic Other trigger.6,14 Other triggers are much less structured than more specific triggers, which could introduce more subjectivity and variability in reviewer interpretation of potential harm. However, if we removed the 18 AEs from the Medication module Other category, our recalculated ADE rate per 100 patients of 17.5 would still be higher than previously published rates.6,15,24–26

GTT Utility in a Pediatric Setting

We observed wide variability in the number and type of AEs detected within the 6 trigger modules. The highest yield of triggers and AEs detected came from the Cares and Medication modules (95%). Only 3 of 28 triggers from these 2 modules did not lead to further investigation, and most trigger types led to the detection of at least 1 AE. The only AEs discovered via the Surgical module were attributed to the “any operative complication” trigger. This module also contained an adult-oriented trigger that would rarely be applicable to our population (S9, postoperative troponin level >1.5 ng/dL). In addition, the Surgical triggers themselves reflect higher severity harm, so it is not entirely surprising that we detected fewer triggers and AEs than were found in the Cares and Medication modules.

No AEs were detected by using the other 3 modules. Together, they have just 13 triggers, and 7 are in the Perinatal module, which is not applicable in our institution because we do not routinely perform planned deliveries. The other 6 triggers are geared more toward adults. A larger sample size review or expansion of the number of triggers would likely capture harm in the ICU. Harm that occurred in an ICU or operating room may have been detected by triggers from other modules such as the Cares and Medication modules. As such, the paucity of harm detected by the GTT’s dedicated Intensive Care and Surgical triggers should not be interpreted as a lack of harm occurring in those regions.

Overall, the GTT proved useful in detecting a wide array of AEs, particularly events associated with the Medication and Cares modules. Despite having several adult-focused triggers and entire modules that captured no AEs during the study, the GTT was effective and detected higher rates of harm than have been previously published in the pediatric trigger tool literature. The presence of adult-oriented triggers and entire modules of less-useful triggers highlights the need for modification of the GTT if it is to be used efficiently in pediatric environments.

Comparison With Adult Benchmarks

The mean rates of AEs per 100 patients and per 1000 patient-days that we observed were less than that reported by the IHI for adult patients.16 The larger difference between our result and IHI’s benchmark result for AEs per 1000 patient-days, in comparison with the difference in AEs per 100 admissions between CCHMC and IHI, was likely due to a longer length of stay for our sample population. However, our rate for AEs per 1000 patient-days was higher than a recent adult study of almost 2400 admissions that found a rate of 68.1 AEs.19 Future research with the GTT in both adult and pediatric populations could provide more data for adjusting benchmarks or determining the amount of variability that exists in baseline rates across different institutions.

Distribution of Harm

Takata et al reported that >97% of ADEs discovered were category E, with the remainder in category F.15 In our study, 76% of AEs were category E, 22% category F, and 2% were category H, representing a shift toward higher levels of harm. One of the fundamental differences between the 2 trigger tools that may account for this distributional difference is that the GTT is designed to capture both ADEs and other AEs, whereas Takata et al’s tool captures only ADEs. If we eliminated all non-ADEs, our distribution of harm would have been 51% category E, 44% category F, and 5% category H. Good et al’s study19 in adults, however, captured a higher proportion of severe events, with 10.1% of all AEs detected in either category H or I. Application of the GTT to a pediatric population yielded a slightly higher distribution of harm than previously published trigger tool data has shown.15

Future Directions

To be even more effective as a pediatric trigger tool, current GTT triggers and modules should be revised to more accurately reflect AEs that occur in children. Modules could also be chosen either for exclusion or pilot testing at individual institutions to determine utility. For example, the Perinatal module would likely not be used in stand-alone pediatric centers that do not have an obstetrical unit. Removal of unnecessary or adult-oriented triggers would reduce the overall number of triggers that reviewers must consider. It would also be helpful to have pediatric benchmarks based on data acquired from several institutions. The data gathered from studies such as this one can be used not only to detect harm, but also to target areas of clinical care for improvement in care delivery and patient safety.

Strengths and Limitations

This study represents the largest published manual trigger tool medical record sampling at a single, all-ages pediatric institution. In addition, previous studies describing the use of other pediatric trigger tools used a distributive model for collecting data, aggregating data from multiple sites and multiple reviewers.6,14,15 In our study, all records were reviewed by the same 3 individuals: 2 primary nurse reviewers and 1 physician validator. The primary reviewers were very experienced, participating in several other ongoing medical record reviews, including studies using other trigger methodologies. Interrater reliability was good and was similar to or exceeded previously published levels of reliability for similar work.20,27–30

Limitations included conducting the study at a single site. As a result, our results may reflect local practice and organizational behavior patterns that may undermine external validity. Comparisons of our data were limited by the lack of studies that used a similar trigger tool. Benchmark rates are also very dependent on multiple factors, such as reviewer experience and methodology, and, as such, comparisons are subject to the inherent variability of these factors. Although there are many pediatric trigger tools now available, there remains a paucity of data on their use because they are still relatively new. The GTT is a true AE detection system, capturing both drug- and non–drug-related AEs, whereas most data available at this time primarily relate to ADE detection only.

Conclusions

Our application of the IHI GTT to a pediatric inpatient population yielded data that represent the highest rates of AEs and ADEs detected by an all-ages pediatric trigger tool yet published. The adult-focused GTT will need further revisions to become a viable pediatric trigger tool, but it exhibits promise in increasing the detection of harm in pediatric inpatients. The discrepancy in the utility of the 6 individual trigger modules should be addressed. More than two-thirds of the AEs we detected were ADEs. AE rates were slightly lower than the adult benchmarks set for the GTT by IHI, but harm detection rates were significantly higher than previously described in the pediatric trigger tool literature.

All of the listed authors are responsible for the reported research; we have participated in the concept and design, analysis and interpretation of data, and drafting and revising of the manuscript; we all approve the manuscript as submitted; Drs Kotagal and Muething, Ms Frese, and Ms Hacker contributed to the conception and design of the project; Ms Hacker, Ms Frese, Ms Kloppenborg, and Dr Kirkendall contributed to the acquisition of the data; all authors contributed to the analysis and interpretation of the data; Ms Hacker, Ms Frese, Dr White, Mr Papp, Ms Kloppenborg, and Ms Schoettker drafted the manuscript; and Drs Muething and Kirkendall and Ms Schoettker contributed to the critical revision of the manuscript for important intellectual content.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

. Large-scale deployment of the Global Trigger Tool across a large hospital system: refinements for the characterisation of adverse events to support patient safety learning opportunities.BMJ Qual Saf. 2011;20(1):25–30pmid:21228072

National Coordinating Council for Medication Error Reporting and Prevention. Types of Medication Errors. Available at: www.nccmerp.org/medErrorCatIndex.html. Updated February 20, 2001. Accessed May 24, 2011

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